Doctoral student in Mathematics with a focus on Computer Vision - Hiring in process/Finished, not possible to apply

Description of the workplace

The position will be placed at the division of Computer Vision and Machine Learning at the Centre for Mathematical Sciences. The Centre for Mathematical Sciences is an institution affiliated with both the Faculty of Engineering (LTH) and the Faculty of Science at Lund University. Within the division of Computer Vision and Machine Learning, there are several senior researchers and approximately 20 doctoral candidates. Research in this field began in the mid-1980s and currently encompasses (i) Geometry and computer vision (including analysis of video, audio, radio, and radar signals), (ii) Medical image analysis, and (iii) Machine learning/artificial intelligence. The group boasts extensive experience in fundamental research within computer vision, machine learning and artificial intelligence, as well as a track record of translating such findings into practical applications for end-users. The department actively participates in numerous projects, such as WASP and ELLIIT, and maintains a robust national and international network. Several products and companies have been launched by researchers within the group, including Decuma, Cognimatics, Polar Rose, Modcam, and Spiideo. The position is funded by the Swedish Research Council and is part of the "Beyond 3D points in Sparse Visual Mapping" project.

Research subject

The research area for the current call is computer vision and machine learning, with a focus on sparse methods for visual mapping. The research subject is mathematics.

Work duties

The main duties of doctoral students are to devote themselves to their research studies which includes participating in research projects and third cycle courses. The work duties will also include teaching and other departmental duties (no more than 20%). The research area for the current call is computer vision and machine learning, with a focus sparse visual localization and mapping.

This project concerns 3D mapping methods which are used to enable re-localization and navigation in large-scale environments. We will focus on sparse reconstruction methods which takes as input is a set of images and produces a sparse map, typically consisting of a 3D point cloud. In the scope of the project, we will extend these methods to also integrate higher-level scene primitives into the reconstruction. The target applications are in Structure-from-Motion (SfM), Simultaneous Localization and Mapping (SLAM) and visual localization. The thesis work will include the development of new methods, planning and execution of experiments, data collection, programming and implementation, writing scientific articles, and presenting the results at international conferences.

Admission requirements

A person meets the general admission requirements for third-cycle courses and study programmes if the applicant:

• has been awarded a second-cycle qualification, or
• has satisfied the requirements for courses comprising at least 240 credits of which at least 60 credits were awarded in the second cycle, or
• has acquired substantially equivalent knowledge in some other way in Sweden or abroad.

A person meets the specific admission requirements for third cycle studies in mathematics if the applicant has:

• at least 90 credits of relevance to the subject area, of which at least 60 credits from the second cycle and a specialized project of at least 30 second-cycle credits in the field, or
• a second second-cycle degree in a relevant subject.

In practice this means that the student should have achieved a level of knowledge in mathematics that corresponds to that of a Master of Science programs in engineering mathematics or engineering physics or a master’s degree in mathematics or applied mathematics.

Additional requirements:

• Very good oral and written proficiency in English.

Assessment criteria

Selection for third-cycle studies is based on the student’s potential to profit from such studies. The assessment of potential is made primarily on the basis of academic results from the first and second cycle. Special attention is paid to the following:

Knowledge and skills relevant to the thesis project and the subject of study. An assessment of ability to work independently and to formulate and tackle research problems. Written and oral communication skills. Other experience relevant to the third-cycle studies, e.g. professional experience.

Other assessment criteria:

• Good programming skills in Python or C++
• Experience in machine learning frameworks (PyTorch, TensorFlow or equivalent).
• Skills in Computer Vision or Machine Learning relevant for the project.

Consideration will also be given to good collaborative skills, drive and independence, and how the applicant, through experience and skills, is deemed to have the abilities necessary for successfully completing the third cycle programme.

We offer

Lund University is a public authority which means that employees get particular benefits, generous annual leave and an advantageous occupational pension scheme.
Read more on the University website about being a Lund University employee Work at Lund University.

Terms of employment

Only those admitted to third cycle studies may be appointed to a doctoral studentship. Third cycle studies at LTH consist of full-time studies for 4 years. A doctoral studentship is a fixed-term employment of a maximum of 5 years (including 20% departmental duties). Doctoral studentships are regulated in the Higher Education Ordinance (1993:100), chapter 5, 1-7 §§.

How to apply

Applications shall be written in English and include a cover letter stating the reasons why you are interested in the position and in what way the research project corresponds to your interests and educational background. The application must also contain a CV, degree certificate or equivalent, grade transcripts and other documents you wish to be considered (contact information for your references, letters of recommendation, etc.).

Welcome to apply!